7 citations · 7 across the 13 of their papers we have counts for
14 papers
Attribute-Grounded Selective Reasoning for Artwork Emotion Understanding with Multimodal Large Language Models
Cheng Zhang, Yuer Liu, Zhiyu Zhou +2
Multimodal large language models (MLLMs) can produce fluent artwork emotion explanations, but they often suffer from attribute flooding: they enumerate many visible formal attribut…
OSDN: Improving Delta Rule with Provable Online Preconditioning in Linear Attention
Chenyu Zhou, Hongpei Li, Yuerou Liu +3
Linear attention and state-space models offer constant-memory alternatives to softmax attention, but often struggle with in-context associative recall. The Delta Rule mitigates thi…
Enhancing Financial Report Question-Answering: A Retrieval-Augmented Generation System with Reranking Analysis
Zhiyuan Cheng, Longying Lai, Yue Liu +2
Financial analysts face significant challenges extracting information from lengthy 10-K reports, which often exceed 100 pages. This paper presents a Retrieval-Augmented Generation…
Aligning Paralinguistic Understanding and Generation in Speech LLMs via Multi-Task Reinforcement Learning
Jingxiang Chen, Minseok Kim, Seong-Gyun Leem +13
Speech large language models (LLMs) observe paralinguistic cues such as prosody, emotion, and non-verbal sounds--crucial for intent understanding. However, leveraging these cues fa…
Sustainable Hybrid Document-Routed Retrieval for Financial RAG: Resolving the Robustness-Precision Trade-off
Zhiyuan Cheng, Longying Lai, Yue Liu
Retrieval-Augmented Generation (RAG) systems for financial document QA typically follow a chunk-based paradigm: documents are split into fragments, embedded, and retrieved by simil…
Stream RAG: Instant and Accurate Spoken Dialogue Systems with Streaming Tool Usage
Siddhant Arora, Haidar Khan, Kai Sun +14
End-to-end speech-in speech-out dialogue systems are emerging as a powerful alternative to traditional ASR-LLM-TTS pipelines, generating more natural, expressive responses with sig…
SCRIBES: Web-Scale Script-Based Semi-Structured Data Extraction with Reinforcement Learning
Shicheng Liu, Kai Sun, Lisheng Fu +8
Semi-structured content in HTML tables, lists, and infoboxes accounts for a substantial share of factual data on the web, yet the formatting complicates usage, and reliably extract…
Knowledge Extraction on Semi-Structured Content: Does It Remain Relevant for Question Answering in the Era of LLMs?
Kai Sun, Yin Huang, Srishti Mehra +11
The advent of Large Language Models (LLMs) has significantly advanced web-based Question Answering (QA) systems over semi-structured content, raising questions about the continued…
Memory-QA: Answering Recall Questions Based on Multimodal Memories
Hongda Jiang, Xinyuan Zhang, Siddhant Garg +10
We introduce Memory-QA, a novel real-world task that involves answering recall questions about visual content from previously stored multimodal memories. This task poses unique cha…
Refine-n-Judge: Curating High-Quality Preference Chains for LLM-Fine-Tuning
Derin Cayir, Renjie Tao, Rashi Rungta +6
Large Language Models (LLMs) have demonstrated remarkable progress through preference-based fine-tuning, which critically depends on the quality of the underlying training data. Wh…
PrismRAG: Boosting RAG Factuality with Distractor Resilience and Strategized Reasoning
Mohammad Kachuee, Teja Gollapudi, Minseok Kim +10
Retrieval-augmented generation (RAG) often falls short when retrieved context includes confusing semi-relevant passages, or when answering questions require deep contextual underst…
ConfRAG: Confidence-Guided Retrieval-Augmenting Generation
Yin Huang, Yifan Ethan Xu, Kai Sun +12
Can Large Language Models (LLMs) be trained to avoid hallucinating factual statements, and can Retrieval-Augmented Generation (RAG) be triggered only when necessary to reduce retri…
VisualLens: Personalization through Task-Agnostic Visual History
Wang Bill Zhu, Deqing Fu, Kai Sun +8
Existing recommendation systems either rely on user interaction logs, such as online shopping history for shopping recommendations, or focus on text signals. However, item-based hi…
AnyMAL: An Efficient and Scalable Any-Modality Augmented Language Model
Seungwhan Moon, Andrea Madotto, Zhaojiang Lin +10
We present Any-Modality Augmented Language Model (AnyMAL), a unified model that reasons over diverse input modality signals (i.e. text, image, video, audio, IMU motion sensor), and…